A central goal of affective neuroscience is to understand the brain systems and mechanisms underlying the evaluation, experience, and expression of emotion. For example, a widely studied and hotly debated issue in the field is the manner in which biophysical responses to emotional stimuli can be characterized, whether by distinct categories or alternatively along dimensions of valence and arousal. Advances in the fields of psychology, neuroscience, and computer science have fostered significant progress in identifying brain regions involved in processing emotion generally; however, consistent and specific neural markers for distinct affective states have yet to be found. This proposal uses a cutting-edge approach to this core, unresolved question in the field by harnessing emerging pattern classification techniques that are capable of detecting subtle yet coordinated signals from an array of sources. The overarching goal is to identify multivariate patterns of behavioral and biological responding to specific affective states and determine whether these states are organized according to categorical or dimensional architectures. By combining psychophysiology (Aim 1) and functional magnetic resonance imaging (fMRI) (Aim 2), these studies will examine how humans respond to emotional stimuli that vary in duration, modality, and categorical nature. Study 1 focuses on distinct emotions elicited by instrumental music and movie clips whereas Study 2 focuses on those elicited by facial and vocal affect. Together, the aims will provide an integrative, computationally-rigorous method to identify biomarkers of specific emotions that are typically overlooked by conventional univariate statistical approaches. Applying machine learning algorithms in this innovative way could be fruitful for identifying how emotional representations are altered in affective disorders, with the potential for developing novel therapeutic targets. Moreover, identifying patterns of overlap between specific emotion categories may further aid efforts to understand comorbidity issues in anxiety and mood disorders.